adding main.py and name_entity.py to a new branch for sentiment anali…

main.py

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+import glob2
+import nltk
+import string
+import scipy
+from collections import Counter
+from nltk.corpus import stopwords
+from nltk.stem.porter import *
+from sklearn.metrics.pairwise import cosine_similarity, linear_kernel
+from sklearn.feature_extraction.text import CountVectorizer
+import numpy as np  # a conventional alias
+import sklearn.feature_extraction.text as text
+import gensim
+from gensim import corpora
+from nltk.corpus import stopwords
+from nltk.stem.wordnet import WordNetLemmatizer
+import string
+import re
+import dateparser
+from name_entity import *
+stop = set(stopwords.words('english'))
+exclude = set(string.punctuation)
+lemma = WordNetLemmatizer()
+
+
+def clean(doc):
+    stop_free = " ".join([i for i in doc.lower().split() if i not in stop])
+    punc_free = ''.join(ch for ch in stop_free if ch not in exclude)
+    normalized = " ".join(lemma.lemmatize(word) for word in punc_free.split())
+    return normalized
+
+
+def main():
+    for folder_name in glob2.glob("DentonDD2/20*/"):
+        folder_items = glob2.glob("%s*.txt" % folder_name)
+        print folder_name, ":", len(folder_items)
+        doc_complete = []
+        for index, filename in enumerate(glob2.glob("%s*.txt" % folder_name)):
+            if(not re.findall("(stopword|xfiles)", filename)):
+                doc_complete.append(open(filename).read())
+        doc_clean = [filter(lambda x: len(x) > 2, clean(doc.decode("ascii", "ignore")).split())
+                     for doc in doc_complete]
+        dictionary = corpora.Dictionary(doc_clean)
+        doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
+        Lda = gensim.models.ldamodel.LdaModel
+        ldamodel = Lda(doc_term_matrix, num_topics=4,
+                       id2word=dictionary, passes=1)
+        topics =  ldamodel.print_topics()
+        for index, doc in enumerate(doc_clean):
+            topic_max =  None
+            topic_value = 0
+            for topic in topics:
+                sum_of_terms = 0
+                col = Counter(doc)
+                for word in re.findall("\"(\w+)\"", topic[1]):
+                    if(word in col):
+                        sum_of_terms+=col[word]
+                if(sum_of_terms >= topic_value):
+                    topic_value =sum_of_terms
+                    topic_max = topic
+            print topic_max
+            sentences = nltk.sent_tokenize(doc_complete[index].decode("ascii", "ignore"))
+            regex = "|".join([word for word in re.findall("\"(\w+)\"", topic_max[1])])
+            important_sentences = [sentence for sentence in sentences if re.findall(regex,sentence) ]
+            print len(important_sentences)
+            name_entities = set()
+            date_time = set()
+            for sentence in  important_sentences:
+                name_entities.update(get_name_entities(sentence))
+                date_time.add(dateparser.parse(sentence))
+
+
+if __name__ == "__main__":
+    main()

name_entity.py

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+import nltk
+
+def extract_entity_names(t):
+    entity_names = []
+
+    if hasattr(t, 'label') and t.label:
+        if t.label() == 'NE':
+            entity_names.append(' '.join([child[0] for child in t]))
+        else:
+            for child in t:
+                entity_names.extend(extract_entity_names(child))
+
+    return entity_names
+def get_name_entities(sample):
+    sentences = nltk.sent_tokenize(sample)
+    tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
+    tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
+    chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)
+    entity_names = []
+    for tree in chunked_sentences:
+        entity_names.extend(extract_entity_names(tree))
+    return  set(entity_names)